Noise in a randomly and sparsely connected excitatory neural network generates the respiratory rhythm

Jean-Francois Vibert M.D.; Efstratios K. Kosmidis

doi:10.1117/12.488736

30 April 2003 Noise in a randomly and sparsely connected excitatory neural network generates the respiratory rhythm

Jean-Francois Vibert M.D., Efstratios K. Kosmidis

Proceedings Volume 5110, Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems; (2003) https://doi.org/10.1117/12.488736
Event: SPIE's First International Symposium on Fluctuations and Noise, 2003, Santa Fe, New Mexico, United States

Abstract

The mechanisms involved in respiratory rhythm and in its persistence along lifetime have not been completely elucidated yet. The debate if they rely on pacemaker units or on the emerging properties of neural networks is still on. We propose a simple model taking advantage of the synaptic noise and allowing to bridge network and pacemaker theories. The pBC (reticular preBotzinger Complex) and PC (pneumotaxic center) are two randomly and sparsely connected excitatory networks. pBC excites PC that in turn, strongly inhibits pBC. As a part of the reticular formation, the pBC, receives many uncorrelated inputs (noise). The model reproduces most of the experimental observations. Once started, the pBC, whose activity is started by synaptic noise, increase of activity is an emerging property of the excitatory network. This activates the PC that in turn inhibits the pBC and starts the expiration. If, for any reason, noise becomes too low, the network becomes silent, and pacemakers become the only active units able to restart a new inspiration. Safety measures of this kind are very much expected in the operation of a system as vital as respiration. Simulations using an enhanced biologically plausible model of neurons fully support the proposed model.

Citation Download Citation

Jean-Francois Vibert M.D. and Efstratios K. Kosmidis "Noise in a randomly and sparsely connected excitatory neural network generates the respiratory rhythm", Proc. SPIE 5110, Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems, (30 April 2003); https://doi.org/10.1117/12.488736

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KEYWORDS

Neurons

Nerve

Axons

Neural networks

In vivo imaging

Phase modulation

Systems modeling

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